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Infant and child survival in Shaanxi, China
Sm. Sci. Med. Vol. 38, No. 4, pp. 609-621, 1994 Copyright0 1994 El&r ScienceLtd
Pergamon
Printed in Great Britain. All rights reserved 0277-9536/94-$6.00 + 0.00
INFANT
AND CHILD
SURVIVAL
IN SHAANXI,
CHINA
XINHUA STEVE REN The
Joint Program on Society and Health, The Health Institute, New England Medical Center and Harvard School of Public Health, 750 Washington Street, P.O. Box 345, Boston, MA 02111, U.S.A.
Abstract-In
the past several decades China has witnessed an unprecedented decline in infant and child mortality. While China’s success in reducing infant and child mortality has been ascribed to the unusual level of government intervention, there is evidence that change and variation have been, and are, influenced by socioeconomic conditions, familial relations as well as biosocial determinants. This study investigates these influences using retrospective report from 1985 survey in Shaanxi province. The study reveals that social revolution in Chinese families is the mediating force linking the broad societal forces and biosocial determinants with infant mortality; whereas social transformation of the Chinese society has a consistent effect on infant as well as child mortality. Key words-social
change,
familial
relations,
biosocial
INTRODUCIION In the past several decades China has witnessed an unprecedented decline in infant and child mortality. In 1954, infant mortality rate was still as high as 139/1000. But within the next two decades infant mortality rate dropped to 47/1000 [l]. The 1982 Census reported a further drop in infant mortality to 35/1000 [2]. By 1990, infant mortality had reached 20/1000, which was among the very lowest in the less developed countries [3]. China’s success in reducing infant and child mortality within a relatively short period of time has become the subject of considerable discussion and debate [4-81. How does China, still a low-income country, succeed in reducing infant and child mortality to a level which is comparable to that of a developed country? Most studies to date have attributed China’s success to the ‘political will’ of the Communist government in launching social reforms. The Communist government’s efforts to liberate women from the feudal bondage of traditional Chinese society and the campaign on literacy and technical education for women have greatly facilitated a marked degree of female autonomy. As argued powerfully by Caldwell [6], the key to the decline of infant and child mortality may be a synergy between the dedication of mass education especially of women and a substantial degree of female autonomy. Others maintain that the unusual level of govern-
*The Chinese primary health care system operates on three levels: (1) brigade health stations, which consist 2-3 doctors serving a population of 1000-3000; (2) commune
health centers, which receive referrals from the brigade health stations; and (3) county hospitals, which receive referrals from the commune health centers. The countylevel has also two other health care units, i.e. epidemic prevention station and MCH station [5].
determinants,
infant
and child survival,
China
ment intervention in improving environmental sanitation, nutrition, and organization of the basic networks of maternal and child health care centers and personnel is probably central to exceptional infant mortality decline in China [5]. Shortly after the 1949 Communist Revolution, campaigns were mounted to mobilize the mass to improve environmental sanitation through eliminating the ‘four pests’ (e.g. rats, flies, mosquitoes, and bedbugs). These efforts were coupled by the government’s emphasis to establish a sound infrastructure of maternal and child health services.* Just during the 3 yr period between 1949 and 1952, the national total of maternal and child health stations and centers increased from 9 to 2379 [5]. The government also made an extraordinary effort to increase the coverage of immunization for infants and children against a host of infectious diseases. By 1990, immunization coverage in China stood at 99% for BCG vaccine, 98% for the three doses of polio vaccine, 97% for the three doses of DPT, and 98% for the single measles shot [3]. These vigorous efforts by the government over the years had undoubtedly left an enormous impact on infant and child survivorship in China [4]. However, the availability of cross-sectional data for provinces and municipalities has restricted these studies to focusing on the broad level of changes in political, economic, and social institutions of Chinese society; whereas little is known about the mechanism or the causal chain linking these broad contextual changes and low infant and child mortality in China. Specifically, these studies have largely neglected the changes which have occurred in the nature of marriage and the family in China in the past several decades, which have been theorized to mediate the effects of broad societal forces on infant and child survivorship [9, lo]. It is within the family where
XINHUA
610
important decisions are made regarding the allocation of survival resources, where the modern health systems interact with the traditional social norms, that most health interventions succeed or fail [I I]. My aim in this analysis is therefore to consider the effects of social change, biosocial determinants as well as aspects of the family that are of potential theoretical importance for infant and child survival study in the less developed countries: change and variation in the family networks and age and sex stratification within the family. More specifically, I attempt to deal with two fundamental questions: (1) how does variation in family relations in China such as mate selection, the incidence of post-marital co-residency with parents, age differences between spouses and communications between spouses influence infant and child survival via proximate determinants of biosocial nature, and (2) to what extent can family and biosocial variables account for the historical increases in infant and child survival? THEORIES
OF
INFANT
AND
CHILD
MORTALITY
IN LDCs
As early as 1979, Caldwell, based on his work on Nigerian Yoruba society showed that there was a strong inverse relationship between maternal education and infant and childhood mortality. Caldwell speculated that social transformation of the family was the main mediating force linking maternal education with infant and childhood mortality. The education of women greatly changed the traditional balance of familial relationships between generations with profound effects on child care [9]. According to Caldwell, in many male dominated traditional societies, education of women provides a wider social networks, new reference groups and authority models and greater identification with the modern world. A woman with schooling is more likely to challenge her mother-in-law, more likely to attempt to communicate with her husband, and more likely to succeed in crystahzing out from the extended family something more akin emotionally. This social revolution in the area of family results in the breakdown of a patriarchal system,* which in turn has led to a shift of the locus of decision-making and resource allocation from the large kin group to the couple. The rise in the status of women within the family in the less developed countries means that mothers will become less ‘fatalistic’ in case their children get sick. Instead of leaving their sick children to God’s hands (the notion that God gives and takes away life), educated mothers are more likely to demand immediate attention to their sick children. Moreover, when
*Patriarchal family refers to family relations dominated by males. It is characterized by patrilocal residence or co-residence with groom’s parents after marriage and by patrilineal inheritance or inheritance through male heirs. Later in the paper, neolocal family or residence refers to residence away from either parents.
STEVE REN
their children get sick educated mothers are in a position to know better and use more efficiently the existing medical facilities [9]. Therefore, the key to low mortality may be a synergy between mass education and egalitarian politics which leads to a greater willingness to make use of health services. And the family is the main locus of this interaction with these exogenous forces and may serve as a means of cushioning the broad contextual influences of society on infant and childhood mortality. Since the Communist Revolution in 1949, family and marriage systems in China have experienced tremendous changes. China started to break gradually away from a traditional and patriarchal family and marriage system, one which advocated filial piety, wifely submission, and the ideal of a large family. There was a move towards freedom of mate selection, a lessening of the elder’s power, a rise in the position of women, and a trend towards some kind of conjugal, nuclear family [12-141. Therefore, it will be interesting to address how the changes in familial relations influence infant and child survival and to what extent familial relations account for the historical decreases in infant and childhood mortality in China. DATA
AND
METHODS
The study used the data from China In-Depth Fertility Survey-Phase I’ (IDFS) conducted in Shaanxi, Hebei provinces and Sanghai municipality in 1985 [IS]. IDFS employs a stratified multi-stage sample design. The ultimate areal units are neighborhood committees in cities and production teams in rural areas. Within each selected area, systematic selection of households is made and within these selected households, ever-married women aged below 50 are interviewed. Detailed descriptions of the sample design and characteristics of the data are given by the Department of Population Statistics of State Statistical Bureau [ 151. This analysis uses only the data from Shaanxi province. Shaanxi is selected mainly because Shaanxi, located in the Northwest part of the country, is largely a rural province characterized by a large percentage of counties situated in the mountainous regions. Social change and social transformation of the family especially with respect to the rise in women’s status within the family are expected to leave their most visible mark on infant and child mortality differentials among the very poor and least-educated rcpions [16]. For the purpose of this study, I have created a data set in which ever-married women are no longer the cases as in the original data set; instead, the new cases are represented by each birth based on the birth order information. The transformed data set includes 11,391 live births, among which 971 died before they reached age 6. Due to the lack of sibling mortality information in the data, the current study follows the conventional infant and child mortality literature by
Infant and child survival in Shaanxi, China assuming that the observations of multiple births from the same mother are independent. As such, the results may contain potentially biased estimates of infant and child mortality determinants [17-181. IDFS is a life retrospective history data set, one which comprises a longitudinal record of when events happened. The data set provides documentation not only of age at death but also of birth history, which includes birth order, birth weight, place of delivery, and breast-feeding status. Moreover, it also provides extensive information on nuptial history and socioeconomic conditions that is essential to analyzing the causal links among the biosocial determinants, familial relations, socioeconomic characteristics, and infant and child mortality. Meanwhile, because the information collected depends upon the recall of events and dates of events in the past, this data collecting procedure presents several potential methodological problems, i.e. omission of events and misreporting of dates of deaths [ 19-201. However, both problems do not seem to be a particular concern in China as in other less developed countries, for in China these problems are ameliorated by a cultural emphasis on knowledge of dates [21]. Respondents in China, even if illiterate, can supply quite reliable dates of events for their children based on the traditional Chinese calender.* Even so, however, it is still difficult for older women to remember the birth weights of the newborns some 30yr before the survey. So the results of birth weight should be interpreted with caution.
*The traditional Chinese calendar consists of a cycle of ‘animal years’ that repeats every 60 yr. In this cycle, there are 12 animals designated for 12 Earthly Branches. Respondents in China usually remember the animal year in which vital events happen. For details, see Coale and Li [21]. tin this paper I have created an index of marriage type into four categories: non-arranged and neolocal; non-arranged but extended; arranged but neolocal; and arranged and extended marriages. While non-arranged and neolocal marriages denote total free marriages, arranged and extended marriages indicate total parental involvement in the marriage process; whereas the other categories signify partial involvement of parents in the marriage process. This index is created out of two considerations: first, the essence of courtship in China has changed far less than the radical decline in marriage arrangement would suggest. Due to the ban of arranged marriage by the marriage law, many marriages, which are arranged by parents and relatives that would be formally viewed as arranged marriages, have now become free marriages; second, over time patrilocal residence has turned more or less into a ritual formality or symbol of family harmony, a result of the compromise between the traditional ideal of co-residence and a shortage of available sons due to a steady decrease in fertility. The chances that young couples will begin married life residing with the husband’s parents have remained stable, suggesting that even many free marriages start in patrilocal residence. Hence, in order to capture the ‘real’ family relations in China it is advisable to combine mate selection and post-nuptial residence.
611
The current study employs discrete-time event history methods, or a series of conditional logistic regressions, which are given as follows [22]: logit p = In[q/l - q] = a + b,x, + b,x, + . . . + b,x, where q is the probability that a child who survived to age x continues to survive in the interval between x and x + n; a is the intercept representing the odds of surviving for children for whom the values of all coefficients equal zero; and b is the vector of coefficients of the covariates, x,. This discrete-time method is chosen for one main consideration, that is, because the determinants of death change as the infant grows older, it is natural that the analysis should be disaggregated [23]. In other words, the dependent variables of the study are the conditional probabilities of surviving the intervals of < I month, l-11 months, and 12-59 months, or neonatal, postneonatal, and early childhood period respectively. SAS logistic procedures will be used to estimate the effects on the log of the odds of being in category one vs being in the other. For the neonatal period, the odds of being in the category of ‘dying’ is 535 deaths/l 1391 live births at risk; whereas the odds of being in the category of ‘survival’ is (11,391-535) or 10,856/l 1,391. The conditional probabilities for postneonatal and early childhood period are 232 deaths/lo,839 at risk and 204/10,188 at risk respectively. Since the conditional probabilities specify that infants will drop out of the analysis for time units in which their events have occurred or censored, 535 (neonatal deaths) and 17 (censored) cases are not then included in the model run on the postneonatal period; and 767 deaths (535 neonatal +232 post-neonatal) and 436 censored cases (17 for neonates +419 for post-neonates) are not included in the model run on the childhood period (Table 1). Tab!e 2 presents the distributions of all covariates in the study, which include three groups, biosocial determinants which include birth order, breastfeeding status, birth weight, mother’s age at birth, gender, and place of delivery; familial variables which include age differences between spouses, marriage type, and communication between spouses,t and social background factors which include types of region, year of birth, mother’s or father’s level of education. Figure 1 provides a heuristic diagram for the causal relationships that need to be examined among socioeconomic conditions, family relations, biosocial determinants, and infant and child survivorship. RESULTS AND DISCUSSION
Descriptive statistics The last column of Table 1 provides some basic characteristics of infant and child mortality in Shaanxi province. Between the period of 1950 and 1985, neonatal infants on average are susceptible to
XINHUA STEVEREN
612
higher mortality (47/1000 live births) than postneonatal infants (20/1000 live births) and toddlers (20/1000 population between 1 and 5 yr of age). This is largely a consequence of the so-called epidemiological transition, i.e. improvement of socioeconomic conditions is associated with a change from a causeof death structure characterized by infectious and parasitic diseases toward one characterized by degenerative disease that usually occurs during the neonatal period [24]. A close examination of the trend of infant and child mortality over time seems to capture this epidemiological transition (Fig. 2). During the early years of Communist rule, while mortality risk for neonatal and post-neonatal infants were almost the same, child mortality risk of 50/1000 was only slightly lower than those of neonatal and post-neonatal infants. This finding is consistent with many developing countries of China’s level of economic development, i.e. malnutrition and infection are the predominant causes that lead to higher post-neonatal and child mortality. By 198&1985, however, both post-neonatal and early childhood mortality dropped to below lO/lOOO, whereas neonatal mortality rate was as high as 22/1000. This decline in the relative size of the post-neonatal and early childhood components seems to indicate that overall prevalence of infectious disease is decreasing and the frequency of congenital defects is increasing as a major cause of childhood mortality [5]. To facilitate comparison, Fig. 3 shows the change in the distribution of deaths among neonatal, postneonatal, and early childhood. Between 1949-1959, about 35% of all infant and child deaths occurred during the neonatal period, and 65% occurred during the post-neonatal and early childhood period. In contrast, the distribution between 198&1985 showed a reversal, e.g. about 65% of all infant and child deaths occurred during the neonatal period and only 35% occurred during the post-neonatal and early childhood period. Bivariate results The bivariate effects of biosocial, familial, and socioeconomic profiles on the conditional probabilities of surviving neonatal, post-neonatal infancy, and early childhood are contained in Tables 3-5 respectively. These results (gross effects) indicate that consistent with previous studies [23,25-261, neonatal and post-neonatal infant and child survivals are influ-
enced by biosocial factors, familial relations, and social determinants. The effects of year of birth on the survivorship are in the expected direction for all three groups. Since 1949, the survival chances have been improving over time, as indicated by an increase in the strength of coefficients. For instance, compared to neonates born prior to 1965 (Table 3), born between 1980-1985 increases the log odds of survival by 1.06, or exp (1.06) = 2.88, which also means that the probability of surviving neonatal infancy for those born in 1980-1985 is almost three times greater than that for those born prior to 1965. Higher risk of mortality is incremental with birth order. Compared to parity 1 infants, being in parity 2 and above decreases the log odds of surviving. For instance, the probability of surviving neonatal infancy for 2nd-order births is only 92% [exp( - 0.08) = 0.921 of that for lst-order infants; whereas for Sth-order births, the probability of surviving neonatal infancy is only 52% [exp( - 0.66) = 0.521 as compared to that for 1St-order births. The explanation seems to lie in the fact that first births are not affected by the presence of a previously born child and therefore have a comparative advantage [19,20]. The termination of breast-feeding during the first year of life tends to increase the risks of dying for neonatal and post-neonatal infants. But the effects are significant only during the neonatal period. Infants who were breastfed for the first year of life also have a better chance of surviving through childhood than those who did not get breast-feeding for the first year of life. As expected, birth weight has tremendous impacts on the survivorship. Very light babies (~4 lb) suffer higher risks of dying. Low birthweight also has a continuous effect on the survivorship of children, a finding consistent with most studies in this area [23]. The survivorship of neonatal, post-neonatal infants and children is also influenced by a mother’s age at birth. Mortality is clearly higher among children of teenage mothers (younger than 18). But contrary to studies in which mortality is found higher among children of mothers aged 35 or over, the results here do not suggest such a general increased risk for infants born to older mothers. Infants who were delivered at home are also less likely to survive as compared to those who were delivered at hospitals. The probability of survival for neonates born at home is only 45% [exp( -0.79) = 0.451 of that for infants
Table I Number of infants and children at risk of dying and mortality live births. Shaanxi. China. 1950-1985 Month at start of interval age group (months) 0: Neonatal (< I) I :Post-neonatal (l-l I) I2 :Childhood ( 12-59) ‘Mortality tMortality
Number at risk
Number of deaths
II391 10839 10188
535 232 204
Number censored I7 419 1543
rates per 1000
Mortality rate 47.0’ 20.4’ 20.07
rate per 1000 live births (n = 11391). rate per 1000 population between I and 5 yr of age (n = 10188).
Infant and child survival in Shaanxi, China Table 2. Descriptive
statistics
of covariates used in the logistic models of infant and child survival in Shaanxi, China, 1950-1985 Post-neonatal
Neonatal (< Mean (1) Biosocial factors Birth order 2 3 4 5f Breastfeeding status: No Birth weight: 641b %6 lb Age of birth: 18-20 21-27 28+ Place of delivery: Home Gender: Girls (2) Social factors Types of region: Mountain Year of birth: 65-69 70-74 75-79 8&85 Education: Father’s illiterate Mother’s illiterate (3) Familial relations Age difference: Husband > wife 1-3 Husband > wife 46 Husband > wife 7+ Marriage type*:
^I f Communication: No discussion No. at risk:
613
I
month) SD
(1-I Mean
I
months) SD
Child (12-59 months) SD Mean
0.262 0.180 0.113 0.115
0.440 0.383 0.316 0.319
0.264 0.179 0.112 0.112
0.384 0.315 0.316
0.262 0.180 0.115 0.115
0.440 0.384 0.319 0.319
0.024
0.151
0.012
0.111
0.012
0.110
0.024 0.582
0.153 0.493
0.020 0.579
0.141 0.493
0.020 0.574
0.138 0.494
0.106 0.63 I 0.253
0.307 0.482 0.435
0.103 0.635 0.252
0.304 0.481 0.434
0.105 0.634 0.252
0.306 0.482 0.434
0.887
0.316
0.884
0.319
0.883
0.321
0.472
0.499
0.474
0.499
0.476
0.499
0.591
0.827
0.584
0.825
0.580
0.823
0.196 0.242 0.190 0.190
0.397 0.428 0.392 0.392
0.194 0.242 0.192 0.194
0.395 0.429 0.394 0.396
0.201 0.253 0.202 0.162
0.40 I 0.434 0.40 I 0.369
0.304 0.656
0.460 0.474
0.298 0.652
0.457 0.476
0.304 0.659
0.457 0.477
0.385 0.252 0.201
0.486 0.434 0.401
0.387 0.249 0.200
0.487 0.432 0.400
0.384 0.252 0.203
0.486 0.434 0.402
0.582 0.036 0.270
0.493 0.188 0.444
0.582 0.035 0.270
0.493 0.184 0.444
0.578 0.035 0.273
0.493 0.183 0.444
0.864 11391
0.342
0.863
0.343
0.862 10188
0.345
0.441
All variables are dummy categories. Therefore, the mean equals percent relative to the omitted category in the table, which are as follows: birth order 1; breastfeeding; birth weight 7+ lb; age at birth Q 17; hospital delivery; plain region; year of birth < 1964; father’s literate; mother’s literate; boys; spouses of the same age; non-arranged marriage and neolocal residence; and discussed family size with husbands. *Marriage type: 1 = non-arranged marriage and extended nsidencc; 2 = arranged marriage and extended residence; 3 = arranged marriage and ncolocal residence.
Proximate determinants Socioeconomic, environmental conditions
Family Relations Fig.
I.
Heuristic
model of the relationships determinants,
among socioeconomic conditions, and infant and child survival.
family relations,
biosocial
614
XINHUA STEVEREN
70 60 50 40 30 20 10 (1959
1965-69
1960-64
1970-74
1975-79
1980-85
Year Source: China In-Depth Fertility SurveyPhase I, 1985. * child mortality per 1000 population.
Fig. 2. Proportion died per 1000 live births by year of birth, Shaanxi, China, 19.5&1985.
born in hospitals (Table 3). The advanced medical facilities and sanitary environment provided by hospitals attribute to this low risk of mortality. But why does place of delivery influence post-neonatal and childhood survivorship when medical facilities are no longer a factor? As will be discussed later, the
access to medical facilities is also a proxy for socioeconomic development of the community. No access to medical facilities is a sign of less development; hence post-neonatal infants and children who reside in such communities tend to have higher risks of dying.
Percent
............................................................................ ............................................................................ ............................................................................ ............................................................................
..::......................... ................... ........... :::::.::
20%0% (1959
1
I 1960-64
1965-69
I 1970-74
I 1975-79
1980-85
Year 0 Source: Chlna Phase I, 1985.
In-Depth
Neonatal Fertlllty
m
Poet-Neonatal
Child
Survey-
Fig. 3. Percent distribution of neonatal, post-neonatal, and child mortality, Shaanxi, China, 19561985.
Infant and child survival in Shaanxi, China
615
Table 3. Effects of biosocial, familial, and socioeconomic determinants on the log odds of surviving neonatal infancy (< I month) Net effects Exolanatorv variables (1) Hisloricol
Gross effect
rrend
Year of birth: 65-69 7&74 75-79 S&85 (2) Biosocial factors Birth order: 2 3 4 5+ Breastfeeding status: NO Birth weight: 44 lb 5-6 lb Age at birth: 18-20 21-27 28+ Place of delivery: Home Gender: Girls (3) Familial and social factors Age difference: Husband > Wife l-3 Husband > Wife 46 Husband > Wife 7+ Marriage type: Non-arranged and extended Arranged and extended Arranged and neolocal Communication: NO Types of region: Mountain Education: Father’s illiterate Mother’s illiterate Intercept N -2 log likelihood
Biosocial factors
Familial and social factors
(2)
(1) 0.24 0.45 0.62 1.06
(0.13)t (0. I,)*‘* (0.14)*** (0.16)***
0.29 0.53 0.71 0.90
- 0.08 -0.21 -0.39 -0.66
(0.12) (0.13) (0.15)‘1 (0.14)***
0.17 -0.01 -0.15 -0.45
(0.14)’ (0.14)*** (0.16)“. (0.18)*** (0.15) (0.16) (0.18) (0.18)‘.
(3) 0.28 0.49 0.69 0.85 0.22 0.05 -0.04 -0.26
(0.14)’ (0.15)*‘* (0. I7)*‘* (0.18)*** (0.15) (0.16) (0.18) (0.18)
-3.27 (0.13)***
-3.34 (0.15)***
-3.50 (0.16)**’
-1.99 (0.18)‘” -0.52 (O.lO)***
- 1.64 (0.2l)“’ -0.45 (0.1 I)***
- I .76 (0.22)“’ -0.48 (0.1 I)“’
0.59 (0.33)t I.13 (0.31)*** 0.90 (0.32)” -0.79 (0.19)*** 0.27 (0.09)”
0.37 (0.35) 0.80 (0.35)’ 0.86 (0.36)**
0.30 (0.35) 0.65 (0.35)t 0.74 (0.36)*
- 1.08 (0.22)**’
-0.68 (0.22)**
0.37 (o.lo)***
0.37 (o.lo)*”
-0.30 (0.15)’ -0.57 (0.16)*** -0.55 (0.16)‘*’
-0.17 (0.15) -0.23 (0.16) - 0.06 (0.17)
-0.52 (0.17)** -1.13 (0.24)“. -0.26 (0.18)”
-0.67 (0.20)*** - I .Ol (0.27)*** -0.54 (0.21)**
-0.28 (0.14)’
-0.12 (0.16)
-0.18 (0.05)‘”
-0.24 (0.06)***
-0.59 (0.09)*** -0.51 (o.lo)*** 2.59’ (0.09)*** 113Yl 4261’***
-0.32 (0.1 I)** -0.25 (0.12)’ 4.39 (0.48)“’ 11391 3575***
3.33 (0.40)*** 11391 3639’**
All variables are dummy variables. For omitted categories see Table 2. The standard errors are reported in parentheses. ‘Statistics for (I), historical trend only. tP < 0.10; lf < 0.05; **P < 0.01; l**P < 0.001 level.
Consistent with previous studies, neonatal mortality is lower for females than for males. Being a girl increases the log odds of surviving neonatal infancy by 0.27, or being a girl increases the ‘hazard’ of surviving neonatal infancy by an estimated 31% [exp(O.27) = 1.311. This is primarily due to genetic differences between males and females. X-linked immunoregulatory genes appear to contribute to greater resistance to infectious diseases for females [27-291. Being a girl also increases the likelihood of surviving through post-neonatal period. This can be ascribed to both the genetic advantages females possess as well as to a unique practice of breast-feeding in China, in which males and females alike are breastfed for at least 1 yr. This has greatly enhanced the survival chances for females. However, being a girl reduces the chances of surviving during early childhood. This is largely a reflection of the persistence of son preference in Chinese society. After the termination of breast-
feeding at the end of the first year, female children, as in some other less developed countries [30,3 11,are usually being discriminated both in terms of unfavorable allocation of scarce survival resources and inferior health care [32, 331. The effects of familial relations on infant and child survivorship are in the expected direction. These family variables-age stratification (or age differences) between spouses, marriage type, and communication-reflect various dimensions of the relative autonomy of females within the family. As Tables 3-5 (gross effects) indicate, age differences between spouses, especially with husbands being 7+ yr older than their wives, have a strong negative influence on infant and child survival. For instance, the probability of surviving post-neonatal for those whose father; are 7+ yr older than their mothers is only 49% [exp( -0.71) = 0.491 of that for those whose parents are of the same age (Table 4). Under a
616
XINHIJA STEVE REN
marriage when a bride shifts residence to an unfamiliar family and community and finds herself surrounded by curious and even hostile in-laws. Neolocal and romantic marriages increase the importance of physical and emotional bonds between spouses and thus tend to increase the likelihood of infancy and child survival due largely to an egalitarian and companionate marriage. On the other hand, infants in partial-arranged and patrilocal marriages have lower survival chances due to the lower social status of mothers in these types of families. Discussion of family size also has strong negative effects on infant and child survival. ‘Discussion over family size between spouses’ may serve as an important proxy for intrafamilial relations. Although such conversations may seem routine in the West, there is a certain reserve or self-sufficiency which discourages free and informal communication in many less
patriarchal family system, age differences may be a proxy for power. In order to ensure male dominance in the family, the husband’s parents may prefer a young daughter-in-law so that she can easily be socialized into her role in her husband’s family. Thus, the larger the age gap between spouses the lower the status a woman occupies in the family, which in turn reduces the survival chances for her infants. Compared to free marriages (non-arranged and neolocal residence), partial-free marriages (non-arranged but extended or arranged but neolocal) tend to reduce survival chances; whereas infants in arranged and patrilocal marriages have the least survival chances. Free mate selection and neolocal residence indicate a higher degree of female autonomy in the family. Women who marry neolocally and romantically do not have to make dramatic psychological adjustments normally required in a patrilocal
Table
4. ElTects of biosocial,
familial, and socioeconomic determinants post-neonatal infancy (I-I I months)
Net effects
-~ Explanatory
variables
Gross elTect
(I) Historical fiend Year of birth: 65-69 7&74 75-79 80-85 (2) Biosocid facfors Birth order: L
3 4 5+ Breastfeeding status: NO Birth weight: <4lb M lb Age at birth: I B-20 21-27 28+ Place of delivery: Home Gender: Girls (3) Familial and social factors Age difference: Husband > Wife l-3 Husband > Wife 46 Husband > Wife 7 + Marriage type: Non-arranged and extended Arranged and extended Arranged and neolocal Communication: NO Types of region: Mountain Education: Father’s illiterate Mother’s illiterate
Intercept N - 2 log likelihood All variables
tP
< 0.10;
historical
lP < 0.05; **P
factors
Familial
and social factors
(2)
(0.18)” (O.lB)*** (0.23)*** (0.25)‘.’
(3)
0.52 0.83 1.18 1.22
(0.19)** (0.20)*‘* (0.24)**’ (0.26)“’
0.52 0.80 I.19 I.21
(0.19).’ (0.20)*** (0.25)*** (0.27)***
-0.03 -0.46 -0.07 -0.61
(0.19) (0.18)” (0.24) (O.Zl)**
-0.30 -0.82 -0.51 -I .20
(0.21) (0.23)“** (0.27)t (0.26)***
-0.27 PO.75 -0.42 - 1.05
(0.21) (0.23)*‘* (0.28) (0.27)***
-0.57
(0.46)
-0.43
(0.47)
-0.50
(0.47)
-2.14 -0.88
(0.32)*‘* (0.16)***
- 1.80 (0.30)*** -0.80 (0.16)***
- I.91 (0.31)*** -0.81 (0.16)**’
0.97 (0.40)‘. I .60 (0.38)*** 1.60 (O/40)***
0.80 (0.41)’ 1.43 (0.42)*** I .65 (0.45)***
-0.98
(0.31)“’
-0.73
0.20 (0.13)
-0.10 -0.35 -0.71
(0.32)’
0.75 (0.42)? 1.32 (0.43)” 1.56 (0.45)*** -0.43
0.31 (0.14)’
(0.33)
0.31 (0.14).
(0.22) (0.23) (0.22)‘**
-0.09 -0.1 -0.35
I
(0.22) (0.23) (0.23)
-0.40 (0.25) - 1.06 (0.35)” -0.53 (0.27)’
-0.42 -0.53 -0.26
(0.26) (0.37) (0.28)
-0.77
(0.26)*’
-0.31
(0.26)
-0.26
(0.07)***
-0.34
(0.08)***
-0.52 (0.13)“’ -0.53 (o.l5)*” 3.14’ (0.1 I)*** 10839 2185”**
are dummy variables.
parentheses. “Statistics for (I)
Biosocial
(1) 0.50 0.78 I .23 I .45
on the log odds of surviving
trend < 0.01;
For omitted
3.40 (0.51)*** 10839 2093***
categories
only.
l**f
< 0.001
level.
see Table
-0.08 (0.14) -0.05 (0.17) 4.30 (0.63)*” 10839 2065**’
2. The standard
errors
are reported
in
617
Infant and child survival in Shaanxi, China Table 5. Effects of biosocial,
familial,
and socioeconomic determinants childhood (12-59 months)
on the log odds of surviving
early
Net effects Exolanatorv
variables
(I) Historical hvtd Year of birth: 6549 7&74 7s-79 8&85 (2) Biosocial factors Birth order: 2 3 4 5+ Breastfeeding status: NO Birth weight: 94lb 1-6 lb Age at birth: 18-20 21-27 28+ Place of delivery: Home Gender: Girls (31 ~ Familial and social factors Age difference: . Husband > Wife l-3 Husband > Wife M Husband > Wife 7+ Marriage type: Non-arranged and extended Arranged and extended Arranged and neolocal Communication: NO Types of region: Mountain Education: Father’s illiterate Mother’s illiterate Intercept N -2 log likelihood
Gross effect
Biosocial
0.56 0.69 1.38 1.81
(0.18)” (0.18)**’ (0.25)*** (0.33)***
factors
Familial
and social factors
(2)
(1) 0.73 0.88 1.51 1.77
(3) 0.66 0.79 I .42 1.65
(0.20)*** (0.20)*** (0.26)*** (0.33).”
(0.20)*** (0.20)*** (0.27)*** (0.34)***
-0.40 -0.53 -0.83 -0.74
(0.22)T (0.24)* (0.25)*** (0.28)**
-0.36 -0.47 -0.73 -0.58
(0.22)t (0.24)T (0.26)** (0.28)*
- 0.72 (0.46)
-0.73
(0.47)
-0.92
(0.47)’
-0.67 -0.10
-0.73 -0.06
(0.4l)T (0.14)
-0.85 -0.07
(0.41)* (0.15)
-0.35 -0.46 -0.69 -0.52
(0.20).+ (0.21)’ (0.23)** (0.24)’
(0.40)t (0.14)
0.44 (0.62) 0.63 (0.59) 0.66 (0.60)
0.25 (0.62) 0.30 (0.62) 0.39 (0.63)
0.23 (0.62) 0.26 (0.63) 0.38 (0.64)
- 1.15 (0.36)***
- 1.01 (0.36)”
-0.61
(0.38)t
-0.15
-0.13
-0.13
(0.14)
(0.14)
(0.14)
I
-0.05 (0.23) 0.04 (0.24) -0.55 (0.23)”
-0.06 (0.22) 0. I9 (0.24) -0.17 (0.24)
-0.42 -0.85 -0.77
(0.28) (0.41)* (0.29)**
-0.43 -0.41 -0.06
(0.29) (0.30) (0.22)
-0.74
(0.27)**
-0.25
(0.28)
-0.18
(0.08)’
-0.17
(0.08)*
-0.69 (0.14)*‘* -0.73 (0.17)*** 3.19s (0.12)*** 10188 1938’***
4.26 (0.70)*** 10188 1906***
-0.29 (O.l5)t -0.30 (0.19) 5.02 (0.81)*** 10188 l885***
All variables are dummy variables. For omitted categories see Table 2. The standard parentheses. ‘Statistics for (I) historical trend only. tP < 0.10; lP < 0.05; l*P < 0.01; l**P < 0.001 level.
developed countries. The reluctance to talk about sexual matters is even stronger. When the amount and scope of husband-wife communication tend to be minimal, even ‘discussion of family size’ tends to play an important role in family relations between spouses [34]. Communication leads to accuracy and understanding, which in turn produces relational satisfaction. Couples who communicate tend to be more companionate in marriage; whereas couples who do not tend to have more traditional outlook on marriage. Infant and childhood survivorship is also influenced by socioeconomic conditions. Closely related to the access to medical facilities and sanitary environment is the geographical location in which families reside. Plain areas are often associated with higher level of socioeconomic development, such as better quality of life, easy transportation and easy access to medical facilities; whereas hilly regions are
errors are reported
in
virtually deprived of all these ‘goodies’ in life. Moreover, living in hilly regions is more susceptible to accidents where the environment can be very hazardous. As a result, residing in hilly regions significantly increases the risk of dying as compared to those living in the plain areas. Also consistent with previous studies [6,35, 361, father’s or mother’s education has a negative effect on the survivorship. Neonatal infants and children whose fathers or mothers are illiterate are less likely to survive as compared to infants whose fathers or mothers are literate. MULTIVARIATE
RESULTS
The net effects on Tables 3-5 represent the multivariate effects of biosocial, familial, and socioeconomic determinants on the conditional probabilities of surviving neonatal, post-neonatal infancy, and
618
XINHUA STEVEREN
early childhood. Since one of the primary interests of the study is to examine to what extent biosocial and familial factors account for the historical decreases in infant and child mortality in China, the analyses start by regressing survival on year of birth, which is the same as the bivariate results of year of birth (model 1). Then, in order to assess the extent to which the other variables account for the effects of date of birth, biosocial variables (model 2) and familial and social variables (model 3) are added to the regressions. Model 2, which regresses survival on year of birth and biosocial factors, shows the relative magnitudes of the effects of year of birth shared with and mediated through the biosocial determinants. As the model indicates, after taking into account the biosocial determinants, date of birth remains a strong predictor of survival for all three groups. If China has experienced changes in the status of women over time, year of birth can be employed as a proxy for that changes. For instance, the increase in the positive effects of gender on neonatal and post-neonatal survivals and the decrease in the negative effects of gender on childhood survival after ‘controlling for year of birth’ probably indicate that the status of women has been improved in China over time. It is interesting to note that after controlling for year of birth, there are reductions in the effects of birth order, mother’s age at birth, and place of delivery on the log odds of survival, though the effects do not drop to zero. This clearly indicates that the effects of year of birth on survivorship are somewhat mediated through these biosocial factors. However, the effects of breastfeeding and birth weight on infant and child survivorship actually increase or remain the same as compared to the gross effects, indicating that the effects of these biosocial factors are also independent of time. This is probably due to the fact that the proportions of breastfeeding and low birth weight babies have remained rather constant over time. For instance, the majority of infants (> 95%) have always been breastfed and only a small number (< 3%) of infants were born with a weight <4 lb (see Table 2). Model 3, which regresses survival on year of birth, biosocial as well as familial and social factors, shows that the effects of year of birth on infant and child survival remain strong even after controlling for biosocial as well as familial and social factors. This suggests two things. First, consistent with the crosssectional data, the broad level changes in political, economic, and social institutions of Chinese society have contributed to this improvement in infant and child survival over time. Second, if year of birth can be a proxy for the gradual improvement of women’s status and for other influences accompanying socioeconomic development such as improving the environment, like better living conditions and better medical facilities, then the social and familial variables included in the current study fail to capture all the dimensions of these changes.
The results of model 3 also suggests that when all the variables are included in the regression, the effects of biosocial determinants on infant and child survivorship remain significant. The effects of breastfeeding and birth weight on infant and child survival are independent of all other variables. However. the effects of a mother’s age at birth and place of delivery have reduced, indicating that these biosocial determinants also alleviate the influence of the familial and social background factors on infant and child survivorship. For instance, Appendix A reveals that marriage type and communication between spouses are closely associated with place of delivery. Women who are involved in more egalitarian relationships with their husbands, as indicated by less parental involvement in the marriage process and positive engagement in communication, are more likely to deliver their newborns in hospital. Good familial relations guarantee the utilization and efficacy of the use of medical facilities. Appendix B. on the other hand, indicates that mother’s age at birth has increased tremendously over the period of 1950 and 1985; whereas more educated women and those residing in more developed plain regions are more likely to deliver their newborns in hospitals. When all variables are included in the regression, the effects of marriage type on neonatal infant survival remain significant. For instance, the probability of survivorship for those neonatal infants who were born in partial-arranged marriages is about 51% [exp( -0.67) = 0.511 of that for those born in total free marriages; whereas the probability of surviving for those in arranged and extended patrilocal marriages is only 36% [exp( - 1.01) = 0.361 of the probability for those born in free and neolocal marriages (Table 3). On the other hand, the effects of age differences between spouses and spousal communication on infant and child survival become nonsignificant. This suggests two things: first, historical change has influenced infant and child survival via other variables that are not included in the current study; and second, the effects of social background factors are mediated by familial relations. For instance, as Appendix C shows, year of birth is closely associated with the family relation factors. Over the period of 1950 to 1985, there are more women engaged in communication with their husbands. Most important, level of mother’s education is positively associated with the quality of marriage. Literate women are more likely to have egalitarian marriages and engage in communication with their husbands. SUMMARY
In the past several decades China has witnessed an unprecedented decline in infant and child mortality. While China’s success in reducing infant mortality has been unusual for the level of government intervention, there is ample evidence that change and variation have been and are influenced by familial
Infant and child survival in Shaanxi, China relations, socioeconomic conditions, and biosocial factors as well. In explaining the mortality differentials, the current study yields several findings that are broadly consistent with the clinical and epidemiological literature. Higher mortality risks are associated with higher parity births, very young age of mothers, lack of breast milk, very light babies (~4 lb), and lack of access to medical facilities as indicated by births delivered at home. The study has identified aspects of familial relations in explicating mortality differentials in China. The three family variables-age stratification (or age difference) between spouses, marriage type, and communication, which reflect various dimensions of the relative autonomy of females within the family, all have negative effects on infant and child survivorship. The higher the status of women in the family, as indicated by a smaller age gap, less parental involvement in marriage arrangement, and active engagement in spousal communication, the higher the likelihood for infants and children to survive in such a familial environment. The results have also partially identified the link between familial relations and infant survivorship. Women who are involved in a more egalitarian relationship with their husbands are more likely to deliver their newborns in hospitals, a clear indication that good familial relations guarantee the utilization or efficacy of the use of medical facilities [9]. The study shows that infant and child survivorship is influenced by socioeconomic conditions. Since the 1949 Communist revolution, the survival chances for infants and children in Shaanxi, China have been improving over time. Infants and children who reside in poor hilly areas and whose fathers or mothers were illiterate are less likely to survive as compared to infants who live in more developed plain regions and whose fathers or mothers were literate. Finally, the study reveals that consistent with cross-sectional data, social change or the broad-level change in political, economic, and social institutions of Chinese society is important in improving infant and child survival in China. The fact that date of birth remains a strong predictor after controlling for social and familial determinants indicates that more researches are warranted in order to tease out all other different dimensions of this social change that has occurred in China over the last several decades. Acknowledgemenrs-This study was supported by a grant from the Henry J. Kaiser Family Foundations. The author would like to thank Sol Levine, Ed Schor, Benjamin Amick, and two anonymous reviewers for their insightful comments on an earlier draft.
2.
3.
4.
5.
6. 7. 8.
9.
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21. REFERENCES 22 1. Jiang Zhenghua, Zhang Weimin and preliminary study of life expectancy China’s population. Paper presenred
Zhu Liwei A at birth for at the Inrer-
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national Seminar on China’s I982 Popularion Census, Beijing, 1984. Wang Weizhi Mortality rates for the Chinese population. In China’s Population Yearbook 1985(Edited by Population Research Center), pp. 247-250. Chinese Academy of Social Sciences, 1986. Grant J. P. The State of the World’s Children 1992. Published for UNICEF. Oxford University Press (1992). Jamison D. T. China’s health care system: policies, organization, inputs and finance. In Good Healfh at Low Cost (Edited by Halstead, Walsh and Warren), pp. 21-32. Proceedings of a Conference held at the Bellagio Conference Center, Bellagio, 1985. Young M. E. and Prost A. Child Health in China. World Bank Staff Working Papers, No. 767. The World Bank, Washington, D.C., 1985. Caldwell J. C. Routes to low mortality in poor countries. Populat. and Dev. Rev. 2, 171-220, 1986. Banister J. China’s Changing Population. Stanford University Press, Calif., 1987. Tu Ping The Effects of breast-feeding and birth intervals on child survival. Ph.D. dissertafion, Berkeley, Calif., 1989. Caldwell J. C. Education as a factor in mortality decline: an examination of Nigerian data. Popular. Stud. 3, 3955413, 1979. Berger P. L. and Richard J. N. To Empower People: The Role of Mediating Structures in Public Policy. American Enterprise Institute for Public Policy Research, Washington, D. C., 1977. Mosley W. H. Child survival: research and policy. In Child Survival: Strategies for Research (Edited by Mosley and Chen), pp. 3-23. Popular. Dev. Rev., A supplement to Vol. 10, 1984. Goode W. World Revolution and Family Pattern. Free Press, London, 1963. Parish W. L. and Martin K. W. Village and Family in Conlemporary China. University of Chicago Press, Chicago, 1978. Pastemak B. Marriage and fertility in Tianjin, China: fifty years of transition. Papers of the East- West population institute, No. 99. East and West Center, Population Institute, Hondulin, 1986. DPSB Department of Population Statistics of State Statistic Bureau. China -In-Depth Fertility Survey (Phase I): Principal Report, Vol. 1. Beijing, 1986. Muhuri P. K. and Preston S. H. Effects of family composition on mortality differentials by sex among children in Matlab, Bangladesh. Pomdat. Dev. Rev. 17. 415434, 1991. Guo Guang. Use of sibling data to estimate family mortality effects in Guatemala. Demography 30, 15-32, 1993. Geronimus A. T. and Sanders K. Maternal youth or family background? On the health disadvantages of infants with teenage mothers. Am. J. Epidemiol. 137, 213-225, 1993. Hobcraft J. N., McDonald J. W. and Rutstein S. 0. Demographic determinants of infant and early child mortality: a comparative analysis. Populat. Stud. 39, 363-385, 1985. Singh S. Evaluation of data quality. In The World Fertiliry Survey: An Assessment (Edited by Cleland and Scott), pp. 618643. Oxford University Press, Oxford, 1987. Coale A. J. and Li Shaomin The effects of see misreporting in China on the calculation of mortality rates by very high ages. Demography 2, 293-301, 1991. Allison P. D. Discrete-time methods for the analvsis of event histories. In Sociological Methodology (Edited by Leinhardt S.), pp. 61-98. Jossey-Bass. Calif., 1982.
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23. DaVanzo J., Butz W. P. and Habicht J. P. How biological and behavioral influences on mortality in Malaysia vary during the first year of life. Populat. Stud. 37, 381402, 1983. 24. Omran A. R. Epidemiologic transition in the United States. Populat. Bull. 32, 2, 1977. 25. Knodel J. and Kintner H. The impact of breast feeding patterns on the biometric analysis of infant mortality. Demography 14, 391409. 1917. 26. Pebley A. R. and Irma T. Elo The relationship of birth spacing and child health. In Internarional Population Conference (IUSSP), pp. 403417, 1989. 27. Shapiro S. The influence of weight, sex, and plurality on neonatal loss in the United States. Am. J. pub/. Hlth nations Hlth 404, 1142-l 153, 1954. 28.’ Waldron I. Sex differences in human mortality: the role of genetic factors. Sot. Sci. Med. 17, 321-333, 1983. 29. Waldron I. Patterns and causes of excess female mortality among children in developing countries. World Hlfh Rep. 40, 194&210. 1987. 30. Chen L. C., Huq E. and D’Souza S. Sex bias in the family allocation of food and health care in rural Bangladesh. Popular. Dez>. Rer. 7, 55-70, 1981. 3 1. Cleland J., Verrall J. and Vaessen M. Preferences of the sex of children and their influence on reproductive
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APPENDIX Association
behavior. World Fertilify Survey Comparative Studies, No. 27. International Statistical Institute, Netherlands, 1983. Ren Xinhua Steve Women’s status, sex preference and infant mortality in China: the case of Shaanxi province. Paper presented at the 1993 Annual Meeting of Population Association of America, 1-3 April, Cincinnati, 1993. Arnold F. and Liu Zhaoxiang Sex preference, fertility, and family planning in China. Popular. Dev. Rev. 12, 221-246, 1986. Bogue D. J. Twenty-five communication obstacles to the success of family planning programs. Communication laboratory community and family study center. Media Monograph 2. University of Chicago, 1975. Lindenbaum S. The influence of maternal education on infant and child mortality in Bangladesh. international Center for Diarrhoeal Disease Research Report, Bangladesh, Dhaka, 1983. Preston S. H. Resources, knowledge and child mortality: a comparison of the US in the late nineteenth century and developing countries today. In Selected Readings in the Cultural, Social and Behavioral Determinants of Health (Edited by Caldwell and Santow), pp. 66-78. Health Transition Center, The Austrian National University, 1989.
A
of place of delivery and marriage type and communication spouses in Shaanxi, China, 1950-1985 Place of delivery Hospital
Marriage type: and 31.4 Non-arranged neolocality (395) and 11.0 Non-arranged extended (732) 5.0 Arranged and neolocality (21) 4.4 Arranged and extended (136) Discussion of family size: 20.3 Yes (312) 9.9 No (972)
Statistics of association
Home
Total
69.9 (862) 89.0 (5903) 95.0 (397) 95.6 (2945)
100.0
79.6 (1229) 90.1 (8878)
Number of deaths appears in parentheses. x2 test of association: ***P < 0.001 level.
between
x2
df
698***
3
144***
1
100.0 100.0 100.0
100.0 100.0
Infant
and child survival
in Shaanxi,
APPENDIX
China
621
B
Association of mother’s age at birth and year of birth, place of delivery and mother’s level of education, and place of delivery and type of region in Shaanxi, China, 1950-1985 Year of birth
< 17
G1964
4.1
196551969
E (17) 0.2 (6) 0.2 (5) 0.1 (2)
197&1974 197551979 1980-1985
Mother’s 18-20 27.5 (570) 11.6 (“75;) (204) 3.5 E (95)
age at birth 21-27 > 28 56.1 (1162) 62.8 (1402) 57.2 (1578) 66.8 (1446) 74.2 (1605)
Total
12.4 (257) 24.8 (553) 35.2 (970) 29.5 (638) 21.4 (462)
Literate Illiterate Type of region: Plain Hilly
df
100.0 100.0 100.0 100.0 100.0
Place of delivery Mother’s education
Statistics of association X2
1361***
12
Statistics of association
Hospital
Home
Total
25.0 (979) 4.1 (305)
75.0 (2930) 95.9 (7177)
100.0
16.7 (1197) 2.1 (87)
83.3 (5983) 97.9 (4124)
100.0
100.0
100.0
X2
df
1129***
1
566;”
1
Number of deaths appear in parentheses. X2 test of association: ***P < 0.001 level.
APPENDIX
C
Associations of year of birth and spousal communication, year of birth and marriage type, mother’s level of education and spousal communication, and marriage type and mother’s level of education in Shaanxi, China, 1950-1985 Spousal Year of birth
communication
Yes
No
5.6 (117) 9.0 (201) 11.0 (303) 17.4 (376) 25.1 (544)
94.4 (1956) 91.0 (2030) 89.0 (2455) 82.6 (1789) 74.9 (1620)
Mother’s education: Literate 20.1 (786) Illiterate 10.1 (755)
79.9 (3123) 89.9 (6727)
<1964 1965-1969 1970-1974 1975-1979 198tk1985
Marriage Mother’s education: Literacy Illiteracy
Non-arranged and neolocal 17.8 (696) 7.5 (561)
Total
Number of deaths appear in parentheses. X2 test of association: ***P < 0.001 level.
df
100.0
100.0 100.0
100.0 100.0
445***
5
220***
1
100.0 100.0 type
Non-arranged and extended 65.2 (2549) 54.6 (4086)
Statistics of association Y2
Arranged extended 7.9 (73)
Arranged neolocal
Statistics of association Total
15.1
100.0
0 (2490)
100.0
X2
662’+*
df
3
Pergamon
Printed in Great Britain. All rights reserved 0277-9536/94-$6.00 + 0.00
INFANT
AND CHILD
SURVIVAL
IN SHAANXI,
CHINA
XINHUA STEVE REN The
Joint Program on Society and Health, The Health Institute, New England Medical Center and Harvard School of Public Health, 750 Washington Street, P.O. Box 345, Boston, MA 02111, U.S.A.
Abstract-In
the past several decades China has witnessed an unprecedented decline in infant and child mortality. While China’s success in reducing infant and child mortality has been ascribed to the unusual level of government intervention, there is evidence that change and variation have been, and are, influenced by socioeconomic conditions, familial relations as well as biosocial determinants. This study investigates these influences using retrospective report from 1985 survey in Shaanxi province. The study reveals that social revolution in Chinese families is the mediating force linking the broad societal forces and biosocial determinants with infant mortality; whereas social transformation of the Chinese society has a consistent effect on infant as well as child mortality. Key words-social
change,
familial
relations,
biosocial
INTRODUCIION In the past several decades China has witnessed an unprecedented decline in infant and child mortality. In 1954, infant mortality rate was still as high as 139/1000. But within the next two decades infant mortality rate dropped to 47/1000 [l]. The 1982 Census reported a further drop in infant mortality to 35/1000 [2]. By 1990, infant mortality had reached 20/1000, which was among the very lowest in the less developed countries [3]. China’s success in reducing infant and child mortality within a relatively short period of time has become the subject of considerable discussion and debate [4-81. How does China, still a low-income country, succeed in reducing infant and child mortality to a level which is comparable to that of a developed country? Most studies to date have attributed China’s success to the ‘political will’ of the Communist government in launching social reforms. The Communist government’s efforts to liberate women from the feudal bondage of traditional Chinese society and the campaign on literacy and technical education for women have greatly facilitated a marked degree of female autonomy. As argued powerfully by Caldwell [6], the key to the decline of infant and child mortality may be a synergy between the dedication of mass education especially of women and a substantial degree of female autonomy. Others maintain that the unusual level of govern-
*The Chinese primary health care system operates on three levels: (1) brigade health stations, which consist 2-3 doctors serving a population of 1000-3000; (2) commune
health centers, which receive referrals from the brigade health stations; and (3) county hospitals, which receive referrals from the commune health centers. The countylevel has also two other health care units, i.e. epidemic prevention station and MCH station [5].
determinants,
infant
and child survival,
China
ment intervention in improving environmental sanitation, nutrition, and organization of the basic networks of maternal and child health care centers and personnel is probably central to exceptional infant mortality decline in China [5]. Shortly after the 1949 Communist Revolution, campaigns were mounted to mobilize the mass to improve environmental sanitation through eliminating the ‘four pests’ (e.g. rats, flies, mosquitoes, and bedbugs). These efforts were coupled by the government’s emphasis to establish a sound infrastructure of maternal and child health services.* Just during the 3 yr period between 1949 and 1952, the national total of maternal and child health stations and centers increased from 9 to 2379 [5]. The government also made an extraordinary effort to increase the coverage of immunization for infants and children against a host of infectious diseases. By 1990, immunization coverage in China stood at 99% for BCG vaccine, 98% for the three doses of polio vaccine, 97% for the three doses of DPT, and 98% for the single measles shot [3]. These vigorous efforts by the government over the years had undoubtedly left an enormous impact on infant and child survivorship in China [4]. However, the availability of cross-sectional data for provinces and municipalities has restricted these studies to focusing on the broad level of changes in political, economic, and social institutions of Chinese society; whereas little is known about the mechanism or the causal chain linking these broad contextual changes and low infant and child mortality in China. Specifically, these studies have largely neglected the changes which have occurred in the nature of marriage and the family in China in the past several decades, which have been theorized to mediate the effects of broad societal forces on infant and child survivorship [9, lo]. It is within the family where
XINHUA
610
important decisions are made regarding the allocation of survival resources, where the modern health systems interact with the traditional social norms, that most health interventions succeed or fail [I I]. My aim in this analysis is therefore to consider the effects of social change, biosocial determinants as well as aspects of the family that are of potential theoretical importance for infant and child survival study in the less developed countries: change and variation in the family networks and age and sex stratification within the family. More specifically, I attempt to deal with two fundamental questions: (1) how does variation in family relations in China such as mate selection, the incidence of post-marital co-residency with parents, age differences between spouses and communications between spouses influence infant and child survival via proximate determinants of biosocial nature, and (2) to what extent can family and biosocial variables account for the historical increases in infant and child survival? THEORIES
OF
INFANT
AND
CHILD
MORTALITY
IN LDCs
As early as 1979, Caldwell, based on his work on Nigerian Yoruba society showed that there was a strong inverse relationship between maternal education and infant and childhood mortality. Caldwell speculated that social transformation of the family was the main mediating force linking maternal education with infant and childhood mortality. The education of women greatly changed the traditional balance of familial relationships between generations with profound effects on child care [9]. According to Caldwell, in many male dominated traditional societies, education of women provides a wider social networks, new reference groups and authority models and greater identification with the modern world. A woman with schooling is more likely to challenge her mother-in-law, more likely to attempt to communicate with her husband, and more likely to succeed in crystahzing out from the extended family something more akin emotionally. This social revolution in the area of family results in the breakdown of a patriarchal system,* which in turn has led to a shift of the locus of decision-making and resource allocation from the large kin group to the couple. The rise in the status of women within the family in the less developed countries means that mothers will become less ‘fatalistic’ in case their children get sick. Instead of leaving their sick children to God’s hands (the notion that God gives and takes away life), educated mothers are more likely to demand immediate attention to their sick children. Moreover, when
*Patriarchal family refers to family relations dominated by males. It is characterized by patrilocal residence or co-residence with groom’s parents after marriage and by patrilineal inheritance or inheritance through male heirs. Later in the paper, neolocal family or residence refers to residence away from either parents.
STEVE REN
their children get sick educated mothers are in a position to know better and use more efficiently the existing medical facilities [9]. Therefore, the key to low mortality may be a synergy between mass education and egalitarian politics which leads to a greater willingness to make use of health services. And the family is the main locus of this interaction with these exogenous forces and may serve as a means of cushioning the broad contextual influences of society on infant and childhood mortality. Since the Communist Revolution in 1949, family and marriage systems in China have experienced tremendous changes. China started to break gradually away from a traditional and patriarchal family and marriage system, one which advocated filial piety, wifely submission, and the ideal of a large family. There was a move towards freedom of mate selection, a lessening of the elder’s power, a rise in the position of women, and a trend towards some kind of conjugal, nuclear family [12-141. Therefore, it will be interesting to address how the changes in familial relations influence infant and child survival and to what extent familial relations account for the historical decreases in infant and childhood mortality in China. DATA
AND
METHODS
The study used the data from China In-Depth Fertility Survey-Phase I’ (IDFS) conducted in Shaanxi, Hebei provinces and Sanghai municipality in 1985 [IS]. IDFS employs a stratified multi-stage sample design. The ultimate areal units are neighborhood committees in cities and production teams in rural areas. Within each selected area, systematic selection of households is made and within these selected households, ever-married women aged below 50 are interviewed. Detailed descriptions of the sample design and characteristics of the data are given by the Department of Population Statistics of State Statistical Bureau [ 151. This analysis uses only the data from Shaanxi province. Shaanxi is selected mainly because Shaanxi, located in the Northwest part of the country, is largely a rural province characterized by a large percentage of counties situated in the mountainous regions. Social change and social transformation of the family especially with respect to the rise in women’s status within the family are expected to leave their most visible mark on infant and child mortality differentials among the very poor and least-educated rcpions [16]. For the purpose of this study, I have created a data set in which ever-married women are no longer the cases as in the original data set; instead, the new cases are represented by each birth based on the birth order information. The transformed data set includes 11,391 live births, among which 971 died before they reached age 6. Due to the lack of sibling mortality information in the data, the current study follows the conventional infant and child mortality literature by
Infant and child survival in Shaanxi, China assuming that the observations of multiple births from the same mother are independent. As such, the results may contain potentially biased estimates of infant and child mortality determinants [17-181. IDFS is a life retrospective history data set, one which comprises a longitudinal record of when events happened. The data set provides documentation not only of age at death but also of birth history, which includes birth order, birth weight, place of delivery, and breast-feeding status. Moreover, it also provides extensive information on nuptial history and socioeconomic conditions that is essential to analyzing the causal links among the biosocial determinants, familial relations, socioeconomic characteristics, and infant and child mortality. Meanwhile, because the information collected depends upon the recall of events and dates of events in the past, this data collecting procedure presents several potential methodological problems, i.e. omission of events and misreporting of dates of deaths [ 19-201. However, both problems do not seem to be a particular concern in China as in other less developed countries, for in China these problems are ameliorated by a cultural emphasis on knowledge of dates [21]. Respondents in China, even if illiterate, can supply quite reliable dates of events for their children based on the traditional Chinese calender.* Even so, however, it is still difficult for older women to remember the birth weights of the newborns some 30yr before the survey. So the results of birth weight should be interpreted with caution.
*The traditional Chinese calendar consists of a cycle of ‘animal years’ that repeats every 60 yr. In this cycle, there are 12 animals designated for 12 Earthly Branches. Respondents in China usually remember the animal year in which vital events happen. For details, see Coale and Li [21]. tin this paper I have created an index of marriage type into four categories: non-arranged and neolocal; non-arranged but extended; arranged but neolocal; and arranged and extended marriages. While non-arranged and neolocal marriages denote total free marriages, arranged and extended marriages indicate total parental involvement in the marriage process; whereas the other categories signify partial involvement of parents in the marriage process. This index is created out of two considerations: first, the essence of courtship in China has changed far less than the radical decline in marriage arrangement would suggest. Due to the ban of arranged marriage by the marriage law, many marriages, which are arranged by parents and relatives that would be formally viewed as arranged marriages, have now become free marriages; second, over time patrilocal residence has turned more or less into a ritual formality or symbol of family harmony, a result of the compromise between the traditional ideal of co-residence and a shortage of available sons due to a steady decrease in fertility. The chances that young couples will begin married life residing with the husband’s parents have remained stable, suggesting that even many free marriages start in patrilocal residence. Hence, in order to capture the ‘real’ family relations in China it is advisable to combine mate selection and post-nuptial residence.
611
The current study employs discrete-time event history methods, or a series of conditional logistic regressions, which are given as follows [22]: logit p = In[q/l - q] = a + b,x, + b,x, + . . . + b,x, where q is the probability that a child who survived to age x continues to survive in the interval between x and x + n; a is the intercept representing the odds of surviving for children for whom the values of all coefficients equal zero; and b is the vector of coefficients of the covariates, x,. This discrete-time method is chosen for one main consideration, that is, because the determinants of death change as the infant grows older, it is natural that the analysis should be disaggregated [23]. In other words, the dependent variables of the study are the conditional probabilities of surviving the intervals of < I month, l-11 months, and 12-59 months, or neonatal, postneonatal, and early childhood period respectively. SAS logistic procedures will be used to estimate the effects on the log of the odds of being in category one vs being in the other. For the neonatal period, the odds of being in the category of ‘dying’ is 535 deaths/l 1391 live births at risk; whereas the odds of being in the category of ‘survival’ is (11,391-535) or 10,856/l 1,391. The conditional probabilities for postneonatal and early childhood period are 232 deaths/lo,839 at risk and 204/10,188 at risk respectively. Since the conditional probabilities specify that infants will drop out of the analysis for time units in which their events have occurred or censored, 535 (neonatal deaths) and 17 (censored) cases are not then included in the model run on the postneonatal period; and 767 deaths (535 neonatal +232 post-neonatal) and 436 censored cases (17 for neonates +419 for post-neonates) are not included in the model run on the childhood period (Table 1). Tab!e 2 presents the distributions of all covariates in the study, which include three groups, biosocial determinants which include birth order, breastfeeding status, birth weight, mother’s age at birth, gender, and place of delivery; familial variables which include age differences between spouses, marriage type, and communication between spouses,t and social background factors which include types of region, year of birth, mother’s or father’s level of education. Figure 1 provides a heuristic diagram for the causal relationships that need to be examined among socioeconomic conditions, family relations, biosocial determinants, and infant and child survivorship. RESULTS AND DISCUSSION
Descriptive statistics The last column of Table 1 provides some basic characteristics of infant and child mortality in Shaanxi province. Between the period of 1950 and 1985, neonatal infants on average are susceptible to
XINHUA STEVEREN
612
higher mortality (47/1000 live births) than postneonatal infants (20/1000 live births) and toddlers (20/1000 population between 1 and 5 yr of age). This is largely a consequence of the so-called epidemiological transition, i.e. improvement of socioeconomic conditions is associated with a change from a causeof death structure characterized by infectious and parasitic diseases toward one characterized by degenerative disease that usually occurs during the neonatal period [24]. A close examination of the trend of infant and child mortality over time seems to capture this epidemiological transition (Fig. 2). During the early years of Communist rule, while mortality risk for neonatal and post-neonatal infants were almost the same, child mortality risk of 50/1000 was only slightly lower than those of neonatal and post-neonatal infants. This finding is consistent with many developing countries of China’s level of economic development, i.e. malnutrition and infection are the predominant causes that lead to higher post-neonatal and child mortality. By 198&1985, however, both post-neonatal and early childhood mortality dropped to below lO/lOOO, whereas neonatal mortality rate was as high as 22/1000. This decline in the relative size of the post-neonatal and early childhood components seems to indicate that overall prevalence of infectious disease is decreasing and the frequency of congenital defects is increasing as a major cause of childhood mortality [5]. To facilitate comparison, Fig. 3 shows the change in the distribution of deaths among neonatal, postneonatal, and early childhood. Between 1949-1959, about 35% of all infant and child deaths occurred during the neonatal period, and 65% occurred during the post-neonatal and early childhood period. In contrast, the distribution between 198&1985 showed a reversal, e.g. about 65% of all infant and child deaths occurred during the neonatal period and only 35% occurred during the post-neonatal and early childhood period. Bivariate results The bivariate effects of biosocial, familial, and socioeconomic profiles on the conditional probabilities of surviving neonatal, post-neonatal infancy, and early childhood are contained in Tables 3-5 respectively. These results (gross effects) indicate that consistent with previous studies [23,25-261, neonatal and post-neonatal infant and child survivals are influ-
enced by biosocial factors, familial relations, and social determinants. The effects of year of birth on the survivorship are in the expected direction for all three groups. Since 1949, the survival chances have been improving over time, as indicated by an increase in the strength of coefficients. For instance, compared to neonates born prior to 1965 (Table 3), born between 1980-1985 increases the log odds of survival by 1.06, or exp (1.06) = 2.88, which also means that the probability of surviving neonatal infancy for those born in 1980-1985 is almost three times greater than that for those born prior to 1965. Higher risk of mortality is incremental with birth order. Compared to parity 1 infants, being in parity 2 and above decreases the log odds of surviving. For instance, the probability of surviving neonatal infancy for 2nd-order births is only 92% [exp( - 0.08) = 0.921 of that for lst-order infants; whereas for Sth-order births, the probability of surviving neonatal infancy is only 52% [exp( - 0.66) = 0.521 as compared to that for 1St-order births. The explanation seems to lie in the fact that first births are not affected by the presence of a previously born child and therefore have a comparative advantage [19,20]. The termination of breast-feeding during the first year of life tends to increase the risks of dying for neonatal and post-neonatal infants. But the effects are significant only during the neonatal period. Infants who were breastfed for the first year of life also have a better chance of surviving through childhood than those who did not get breast-feeding for the first year of life. As expected, birth weight has tremendous impacts on the survivorship. Very light babies (~4 lb) suffer higher risks of dying. Low birthweight also has a continuous effect on the survivorship of children, a finding consistent with most studies in this area [23]. The survivorship of neonatal, post-neonatal infants and children is also influenced by a mother’s age at birth. Mortality is clearly higher among children of teenage mothers (younger than 18). But contrary to studies in which mortality is found higher among children of mothers aged 35 or over, the results here do not suggest such a general increased risk for infants born to older mothers. Infants who were delivered at home are also less likely to survive as compared to those who were delivered at hospitals. The probability of survival for neonates born at home is only 45% [exp( -0.79) = 0.451 of that for infants
Table I Number of infants and children at risk of dying and mortality live births. Shaanxi. China. 1950-1985 Month at start of interval age group (months) 0: Neonatal (< I) I :Post-neonatal (l-l I) I2 :Childhood ( 12-59) ‘Mortality tMortality
Number at risk
Number of deaths
II391 10839 10188
535 232 204
Number censored I7 419 1543
rates per 1000
Mortality rate 47.0’ 20.4’ 20.07
rate per 1000 live births (n = 11391). rate per 1000 population between I and 5 yr of age (n = 10188).
Infant and child survival in Shaanxi, China Table 2. Descriptive
statistics
of covariates used in the logistic models of infant and child survival in Shaanxi, China, 1950-1985 Post-neonatal
Neonatal (< Mean (1) Biosocial factors Birth order 2 3 4 5f Breastfeeding status: No Birth weight: 641b %6 lb Age of birth: 18-20 21-27 28+ Place of delivery: Home Gender: Girls (2) Social factors Types of region: Mountain Year of birth: 65-69 70-74 75-79 8&85 Education: Father’s illiterate Mother’s illiterate (3) Familial relations Age difference: Husband > wife 1-3 Husband > wife 46 Husband > wife 7+ Marriage type*:
^I f Communication: No discussion No. at risk:
613
I
month) SD
(1-I Mean
I
months) SD
Child (12-59 months) SD Mean
0.262 0.180 0.113 0.115
0.440 0.383 0.316 0.319
0.264 0.179 0.112 0.112
0.384 0.315 0.316
0.262 0.180 0.115 0.115
0.440 0.384 0.319 0.319
0.024
0.151
0.012
0.111
0.012
0.110
0.024 0.582
0.153 0.493
0.020 0.579
0.141 0.493
0.020 0.574
0.138 0.494
0.106 0.63 I 0.253
0.307 0.482 0.435
0.103 0.635 0.252
0.304 0.481 0.434
0.105 0.634 0.252
0.306 0.482 0.434
0.887
0.316
0.884
0.319
0.883
0.321
0.472
0.499
0.474
0.499
0.476
0.499
0.591
0.827
0.584
0.825
0.580
0.823
0.196 0.242 0.190 0.190
0.397 0.428 0.392 0.392
0.194 0.242 0.192 0.194
0.395 0.429 0.394 0.396
0.201 0.253 0.202 0.162
0.40 I 0.434 0.40 I 0.369
0.304 0.656
0.460 0.474
0.298 0.652
0.457 0.476
0.304 0.659
0.457 0.477
0.385 0.252 0.201
0.486 0.434 0.401
0.387 0.249 0.200
0.487 0.432 0.400
0.384 0.252 0.203
0.486 0.434 0.402
0.582 0.036 0.270
0.493 0.188 0.444
0.582 0.035 0.270
0.493 0.184 0.444
0.578 0.035 0.273
0.493 0.183 0.444
0.864 11391
0.342
0.863
0.343
0.862 10188
0.345
0.441
All variables are dummy categories. Therefore, the mean equals percent relative to the omitted category in the table, which are as follows: birth order 1; breastfeeding; birth weight 7+ lb; age at birth Q 17; hospital delivery; plain region; year of birth < 1964; father’s literate; mother’s literate; boys; spouses of the same age; non-arranged marriage and neolocal residence; and discussed family size with husbands. *Marriage type: 1 = non-arranged marriage and extended nsidencc; 2 = arranged marriage and extended residence; 3 = arranged marriage and ncolocal residence.
Proximate determinants Socioeconomic, environmental conditions
Family Relations Fig.
I.
Heuristic
model of the relationships determinants,
among socioeconomic conditions, and infant and child survival.
family relations,
biosocial
614
XINHUA STEVEREN
70 60 50 40 30 20 10 (1959
1965-69
1960-64
1970-74
1975-79
1980-85
Year Source: China In-Depth Fertility SurveyPhase I, 1985. * child mortality per 1000 population.
Fig. 2. Proportion died per 1000 live births by year of birth, Shaanxi, China, 19.5&1985.
born in hospitals (Table 3). The advanced medical facilities and sanitary environment provided by hospitals attribute to this low risk of mortality. But why does place of delivery influence post-neonatal and childhood survivorship when medical facilities are no longer a factor? As will be discussed later, the
access to medical facilities is also a proxy for socioeconomic development of the community. No access to medical facilities is a sign of less development; hence post-neonatal infants and children who reside in such communities tend to have higher risks of dying.
Percent
............................................................................ ............................................................................ ............................................................................ ............................................................................
..::......................... ................... ........... :::::.::
20%0% (1959
1
I 1960-64
1965-69
I 1970-74
I 1975-79
1980-85
Year 0 Source: Chlna Phase I, 1985.
In-Depth
Neonatal Fertlllty
m
Poet-Neonatal
Child
Survey-
Fig. 3. Percent distribution of neonatal, post-neonatal, and child mortality, Shaanxi, China, 19561985.
Infant and child survival in Shaanxi, China
615
Table 3. Effects of biosocial, familial, and socioeconomic determinants on the log odds of surviving neonatal infancy (< I month) Net effects Exolanatorv variables (1) Hisloricol
Gross effect
rrend
Year of birth: 65-69 7&74 75-79 S&85 (2) Biosocial factors Birth order: 2 3 4 5+ Breastfeeding status: NO Birth weight: 44 lb 5-6 lb Age at birth: 18-20 21-27 28+ Place of delivery: Home Gender: Girls (3) Familial and social factors Age difference: Husband > Wife l-3 Husband > Wife 46 Husband > Wife 7+ Marriage type: Non-arranged and extended Arranged and extended Arranged and neolocal Communication: NO Types of region: Mountain Education: Father’s illiterate Mother’s illiterate Intercept N -2 log likelihood
Biosocial factors
Familial and social factors
(2)
(1) 0.24 0.45 0.62 1.06
(0.13)t (0. I,)*‘* (0.14)*** (0.16)***
0.29 0.53 0.71 0.90
- 0.08 -0.21 -0.39 -0.66
(0.12) (0.13) (0.15)‘1 (0.14)***
0.17 -0.01 -0.15 -0.45
(0.14)’ (0.14)*** (0.16)“. (0.18)*** (0.15) (0.16) (0.18) (0.18)‘.
(3) 0.28 0.49 0.69 0.85 0.22 0.05 -0.04 -0.26
(0.14)’ (0.15)*‘* (0. I7)*‘* (0.18)*** (0.15) (0.16) (0.18) (0.18)
-3.27 (0.13)***
-3.34 (0.15)***
-3.50 (0.16)**’
-1.99 (0.18)‘” -0.52 (O.lO)***
- 1.64 (0.2l)“’ -0.45 (0.1 I)***
- I .76 (0.22)“’ -0.48 (0.1 I)“’
0.59 (0.33)t I.13 (0.31)*** 0.90 (0.32)” -0.79 (0.19)*** 0.27 (0.09)”
0.37 (0.35) 0.80 (0.35)’ 0.86 (0.36)**
0.30 (0.35) 0.65 (0.35)t 0.74 (0.36)*
- 1.08 (0.22)**’
-0.68 (0.22)**
0.37 (o.lo)***
0.37 (o.lo)*”
-0.30 (0.15)’ -0.57 (0.16)*** -0.55 (0.16)‘*’
-0.17 (0.15) -0.23 (0.16) - 0.06 (0.17)
-0.52 (0.17)** -1.13 (0.24)“. -0.26 (0.18)”
-0.67 (0.20)*** - I .Ol (0.27)*** -0.54 (0.21)**
-0.28 (0.14)’
-0.12 (0.16)
-0.18 (0.05)‘”
-0.24 (0.06)***
-0.59 (0.09)*** -0.51 (o.lo)*** 2.59’ (0.09)*** 113Yl 4261’***
-0.32 (0.1 I)** -0.25 (0.12)’ 4.39 (0.48)“’ 11391 3575***
3.33 (0.40)*** 11391 3639’**
All variables are dummy variables. For omitted categories see Table 2. The standard errors are reported in parentheses. ‘Statistics for (I), historical trend only. tP < 0.10; lf < 0.05; **P < 0.01; l**P < 0.001 level.
Consistent with previous studies, neonatal mortality is lower for females than for males. Being a girl increases the log odds of surviving neonatal infancy by 0.27, or being a girl increases the ‘hazard’ of surviving neonatal infancy by an estimated 31% [exp(O.27) = 1.311. This is primarily due to genetic differences between males and females. X-linked immunoregulatory genes appear to contribute to greater resistance to infectious diseases for females [27-291. Being a girl also increases the likelihood of surviving through post-neonatal period. This can be ascribed to both the genetic advantages females possess as well as to a unique practice of breast-feeding in China, in which males and females alike are breastfed for at least 1 yr. This has greatly enhanced the survival chances for females. However, being a girl reduces the chances of surviving during early childhood. This is largely a reflection of the persistence of son preference in Chinese society. After the termination of breast-
feeding at the end of the first year, female children, as in some other less developed countries [30,3 11,are usually being discriminated both in terms of unfavorable allocation of scarce survival resources and inferior health care [32, 331. The effects of familial relations on infant and child survivorship are in the expected direction. These family variables-age stratification (or age differences) between spouses, marriage type, and communication-reflect various dimensions of the relative autonomy of females within the family. As Tables 3-5 (gross effects) indicate, age differences between spouses, especially with husbands being 7+ yr older than their wives, have a strong negative influence on infant and child survival. For instance, the probability of surviving post-neonatal for those whose father; are 7+ yr older than their mothers is only 49% [exp( -0.71) = 0.491 of that for those whose parents are of the same age (Table 4). Under a
616
XINHIJA STEVE REN
marriage when a bride shifts residence to an unfamiliar family and community and finds herself surrounded by curious and even hostile in-laws. Neolocal and romantic marriages increase the importance of physical and emotional bonds between spouses and thus tend to increase the likelihood of infancy and child survival due largely to an egalitarian and companionate marriage. On the other hand, infants in partial-arranged and patrilocal marriages have lower survival chances due to the lower social status of mothers in these types of families. Discussion of family size also has strong negative effects on infant and child survival. ‘Discussion over family size between spouses’ may serve as an important proxy for intrafamilial relations. Although such conversations may seem routine in the West, there is a certain reserve or self-sufficiency which discourages free and informal communication in many less
patriarchal family system, age differences may be a proxy for power. In order to ensure male dominance in the family, the husband’s parents may prefer a young daughter-in-law so that she can easily be socialized into her role in her husband’s family. Thus, the larger the age gap between spouses the lower the status a woman occupies in the family, which in turn reduces the survival chances for her infants. Compared to free marriages (non-arranged and neolocal residence), partial-free marriages (non-arranged but extended or arranged but neolocal) tend to reduce survival chances; whereas infants in arranged and patrilocal marriages have the least survival chances. Free mate selection and neolocal residence indicate a higher degree of female autonomy in the family. Women who marry neolocally and romantically do not have to make dramatic psychological adjustments normally required in a patrilocal
Table
4. ElTects of biosocial,
familial, and socioeconomic determinants post-neonatal infancy (I-I I months)
Net effects
-~ Explanatory
variables
Gross elTect
(I) Historical fiend Year of birth: 65-69 7&74 75-79 80-85 (2) Biosocid facfors Birth order: L
3 4 5+ Breastfeeding status: NO Birth weight: <4lb M lb Age at birth: I B-20 21-27 28+ Place of delivery: Home Gender: Girls (3) Familial and social factors Age difference: Husband > Wife l-3 Husband > Wife 46 Husband > Wife 7 + Marriage type: Non-arranged and extended Arranged and extended Arranged and neolocal Communication: NO Types of region: Mountain Education: Father’s illiterate Mother’s illiterate
Intercept N - 2 log likelihood All variables
tP
< 0.10;
historical
lP < 0.05; **P
factors
Familial
and social factors
(2)
(0.18)” (O.lB)*** (0.23)*** (0.25)‘.’
(3)
0.52 0.83 1.18 1.22
(0.19)** (0.20)*‘* (0.24)**’ (0.26)“’
0.52 0.80 I.19 I.21
(0.19).’ (0.20)*** (0.25)*** (0.27)***
-0.03 -0.46 -0.07 -0.61
(0.19) (0.18)” (0.24) (O.Zl)**
-0.30 -0.82 -0.51 -I .20
(0.21) (0.23)“** (0.27)t (0.26)***
-0.27 PO.75 -0.42 - 1.05
(0.21) (0.23)*‘* (0.28) (0.27)***
-0.57
(0.46)
-0.43
(0.47)
-0.50
(0.47)
-2.14 -0.88
(0.32)*‘* (0.16)***
- 1.80 (0.30)*** -0.80 (0.16)***
- I.91 (0.31)*** -0.81 (0.16)**’
0.97 (0.40)‘. I .60 (0.38)*** 1.60 (O/40)***
0.80 (0.41)’ 1.43 (0.42)*** I .65 (0.45)***
-0.98
(0.31)“’
-0.73
0.20 (0.13)
-0.10 -0.35 -0.71
(0.32)’
0.75 (0.42)? 1.32 (0.43)” 1.56 (0.45)*** -0.43
0.31 (0.14)’
(0.33)
0.31 (0.14).
(0.22) (0.23) (0.22)‘**
-0.09 -0.1 -0.35
I
(0.22) (0.23) (0.23)
-0.40 (0.25) - 1.06 (0.35)” -0.53 (0.27)’
-0.42 -0.53 -0.26
(0.26) (0.37) (0.28)
-0.77
(0.26)*’
-0.31
(0.26)
-0.26
(0.07)***
-0.34
(0.08)***
-0.52 (0.13)“’ -0.53 (o.l5)*” 3.14’ (0.1 I)*** 10839 2185”**
are dummy variables.
parentheses. “Statistics for (I)
Biosocial
(1) 0.50 0.78 I .23 I .45
on the log odds of surviving
trend < 0.01;
For omitted
3.40 (0.51)*** 10839 2093***
categories
only.
l**f
< 0.001
level.
see Table
-0.08 (0.14) -0.05 (0.17) 4.30 (0.63)*” 10839 2065**’
2. The standard
errors
are reported
in
617
Infant and child survival in Shaanxi, China Table 5. Effects of biosocial,
familial,
and socioeconomic determinants childhood (12-59 months)
on the log odds of surviving
early
Net effects Exolanatorv
variables
(I) Historical hvtd Year of birth: 6549 7&74 7s-79 8&85 (2) Biosocial factors Birth order: 2 3 4 5+ Breastfeeding status: NO Birth weight: 94lb 1-6 lb Age at birth: 18-20 21-27 28+ Place of delivery: Home Gender: Girls (31 ~ Familial and social factors Age difference: . Husband > Wife l-3 Husband > Wife M Husband > Wife 7+ Marriage type: Non-arranged and extended Arranged and extended Arranged and neolocal Communication: NO Types of region: Mountain Education: Father’s illiterate Mother’s illiterate Intercept N -2 log likelihood
Gross effect
Biosocial
0.56 0.69 1.38 1.81
(0.18)” (0.18)**’ (0.25)*** (0.33)***
factors
Familial
and social factors
(2)
(1) 0.73 0.88 1.51 1.77
(3) 0.66 0.79 I .42 1.65
(0.20)*** (0.20)*** (0.26)*** (0.33).”
(0.20)*** (0.20)*** (0.27)*** (0.34)***
-0.40 -0.53 -0.83 -0.74
(0.22)T (0.24)* (0.25)*** (0.28)**
-0.36 -0.47 -0.73 -0.58
(0.22)t (0.24)T (0.26)** (0.28)*
- 0.72 (0.46)
-0.73
(0.47)
-0.92
(0.47)’
-0.67 -0.10
-0.73 -0.06
(0.4l)T (0.14)
-0.85 -0.07
(0.41)* (0.15)
-0.35 -0.46 -0.69 -0.52
(0.20).+ (0.21)’ (0.23)** (0.24)’
(0.40)t (0.14)
0.44 (0.62) 0.63 (0.59) 0.66 (0.60)
0.25 (0.62) 0.30 (0.62) 0.39 (0.63)
0.23 (0.62) 0.26 (0.63) 0.38 (0.64)
- 1.15 (0.36)***
- 1.01 (0.36)”
-0.61
(0.38)t
-0.15
-0.13
-0.13
(0.14)
(0.14)
(0.14)
I
-0.05 (0.23) 0.04 (0.24) -0.55 (0.23)”
-0.06 (0.22) 0. I9 (0.24) -0.17 (0.24)
-0.42 -0.85 -0.77
(0.28) (0.41)* (0.29)**
-0.43 -0.41 -0.06
(0.29) (0.30) (0.22)
-0.74
(0.27)**
-0.25
(0.28)
-0.18
(0.08)’
-0.17
(0.08)*
-0.69 (0.14)*‘* -0.73 (0.17)*** 3.19s (0.12)*** 10188 1938’***
4.26 (0.70)*** 10188 1906***
-0.29 (O.l5)t -0.30 (0.19) 5.02 (0.81)*** 10188 l885***
All variables are dummy variables. For omitted categories see Table 2. The standard parentheses. ‘Statistics for (I) historical trend only. tP < 0.10; lP < 0.05; l*P < 0.01; l**P < 0.001 level.
developed countries. The reluctance to talk about sexual matters is even stronger. When the amount and scope of husband-wife communication tend to be minimal, even ‘discussion of family size’ tends to play an important role in family relations between spouses [34]. Communication leads to accuracy and understanding, which in turn produces relational satisfaction. Couples who communicate tend to be more companionate in marriage; whereas couples who do not tend to have more traditional outlook on marriage. Infant and childhood survivorship is also influenced by socioeconomic conditions. Closely related to the access to medical facilities and sanitary environment is the geographical location in which families reside. Plain areas are often associated with higher level of socioeconomic development, such as better quality of life, easy transportation and easy access to medical facilities; whereas hilly regions are
errors are reported
in
virtually deprived of all these ‘goodies’ in life. Moreover, living in hilly regions is more susceptible to accidents where the environment can be very hazardous. As a result, residing in hilly regions significantly increases the risk of dying as compared to those living in the plain areas. Also consistent with previous studies [6,35, 361, father’s or mother’s education has a negative effect on the survivorship. Neonatal infants and children whose fathers or mothers are illiterate are less likely to survive as compared to infants whose fathers or mothers are literate. MULTIVARIATE
RESULTS
The net effects on Tables 3-5 represent the multivariate effects of biosocial, familial, and socioeconomic determinants on the conditional probabilities of surviving neonatal, post-neonatal infancy, and
618
XINHUA STEVEREN
early childhood. Since one of the primary interests of the study is to examine to what extent biosocial and familial factors account for the historical decreases in infant and child mortality in China, the analyses start by regressing survival on year of birth, which is the same as the bivariate results of year of birth (model 1). Then, in order to assess the extent to which the other variables account for the effects of date of birth, biosocial variables (model 2) and familial and social variables (model 3) are added to the regressions. Model 2, which regresses survival on year of birth and biosocial factors, shows the relative magnitudes of the effects of year of birth shared with and mediated through the biosocial determinants. As the model indicates, after taking into account the biosocial determinants, date of birth remains a strong predictor of survival for all three groups. If China has experienced changes in the status of women over time, year of birth can be employed as a proxy for that changes. For instance, the increase in the positive effects of gender on neonatal and post-neonatal survivals and the decrease in the negative effects of gender on childhood survival after ‘controlling for year of birth’ probably indicate that the status of women has been improved in China over time. It is interesting to note that after controlling for year of birth, there are reductions in the effects of birth order, mother’s age at birth, and place of delivery on the log odds of survival, though the effects do not drop to zero. This clearly indicates that the effects of year of birth on survivorship are somewhat mediated through these biosocial factors. However, the effects of breastfeeding and birth weight on infant and child survivorship actually increase or remain the same as compared to the gross effects, indicating that the effects of these biosocial factors are also independent of time. This is probably due to the fact that the proportions of breastfeeding and low birth weight babies have remained rather constant over time. For instance, the majority of infants (> 95%) have always been breastfed and only a small number (< 3%) of infants were born with a weight <4 lb (see Table 2). Model 3, which regresses survival on year of birth, biosocial as well as familial and social factors, shows that the effects of year of birth on infant and child survival remain strong even after controlling for biosocial as well as familial and social factors. This suggests two things. First, consistent with the crosssectional data, the broad level changes in political, economic, and social institutions of Chinese society have contributed to this improvement in infant and child survival over time. Second, if year of birth can be a proxy for the gradual improvement of women’s status and for other influences accompanying socioeconomic development such as improving the environment, like better living conditions and better medical facilities, then the social and familial variables included in the current study fail to capture all the dimensions of these changes.
The results of model 3 also suggests that when all the variables are included in the regression, the effects of biosocial determinants on infant and child survivorship remain significant. The effects of breastfeeding and birth weight on infant and child survival are independent of all other variables. However. the effects of a mother’s age at birth and place of delivery have reduced, indicating that these biosocial determinants also alleviate the influence of the familial and social background factors on infant and child survivorship. For instance, Appendix A reveals that marriage type and communication between spouses are closely associated with place of delivery. Women who are involved in more egalitarian relationships with their husbands, as indicated by less parental involvement in the marriage process and positive engagement in communication, are more likely to deliver their newborns in hospital. Good familial relations guarantee the utilization and efficacy of the use of medical facilities. Appendix B. on the other hand, indicates that mother’s age at birth has increased tremendously over the period of 1950 and 1985; whereas more educated women and those residing in more developed plain regions are more likely to deliver their newborns in hospitals. When all variables are included in the regression, the effects of marriage type on neonatal infant survival remain significant. For instance, the probability of survivorship for those neonatal infants who were born in partial-arranged marriages is about 51% [exp( -0.67) = 0.511 of that for those born in total free marriages; whereas the probability of surviving for those in arranged and extended patrilocal marriages is only 36% [exp( - 1.01) = 0.361 of the probability for those born in free and neolocal marriages (Table 3). On the other hand, the effects of age differences between spouses and spousal communication on infant and child survival become nonsignificant. This suggests two things: first, historical change has influenced infant and child survival via other variables that are not included in the current study; and second, the effects of social background factors are mediated by familial relations. For instance, as Appendix C shows, year of birth is closely associated with the family relation factors. Over the period of 1950 to 1985, there are more women engaged in communication with their husbands. Most important, level of mother’s education is positively associated with the quality of marriage. Literate women are more likely to have egalitarian marriages and engage in communication with their husbands. SUMMARY
In the past several decades China has witnessed an unprecedented decline in infant and child mortality. While China’s success in reducing infant mortality has been unusual for the level of government intervention, there is ample evidence that change and variation have been and are influenced by familial
Infant and child survival in Shaanxi, China relations, socioeconomic conditions, and biosocial factors as well. In explaining the mortality differentials, the current study yields several findings that are broadly consistent with the clinical and epidemiological literature. Higher mortality risks are associated with higher parity births, very young age of mothers, lack of breast milk, very light babies (~4 lb), and lack of access to medical facilities as indicated by births delivered at home. The study has identified aspects of familial relations in explicating mortality differentials in China. The three family variables-age stratification (or age difference) between spouses, marriage type, and communication, which reflect various dimensions of the relative autonomy of females within the family, all have negative effects on infant and child survivorship. The higher the status of women in the family, as indicated by a smaller age gap, less parental involvement in marriage arrangement, and active engagement in spousal communication, the higher the likelihood for infants and children to survive in such a familial environment. The results have also partially identified the link between familial relations and infant survivorship. Women who are involved in a more egalitarian relationship with their husbands are more likely to deliver their newborns in hospitals, a clear indication that good familial relations guarantee the utilization or efficacy of the use of medical facilities [9]. The study shows that infant and child survivorship is influenced by socioeconomic conditions. Since the 1949 Communist revolution, the survival chances for infants and children in Shaanxi, China have been improving over time. Infants and children who reside in poor hilly areas and whose fathers or mothers were illiterate are less likely to survive as compared to infants who live in more developed plain regions and whose fathers or mothers were literate. Finally, the study reveals that consistent with cross-sectional data, social change or the broad-level change in political, economic, and social institutions of Chinese society is important in improving infant and child survival in China. The fact that date of birth remains a strong predictor after controlling for social and familial determinants indicates that more researches are warranted in order to tease out all other different dimensions of this social change that has occurred in China over the last several decades. Acknowledgemenrs-This study was supported by a grant from the Henry J. Kaiser Family Foundations. The author would like to thank Sol Levine, Ed Schor, Benjamin Amick, and two anonymous reviewers for their insightful comments on an earlier draft.
2.
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national Seminar on China’s I982 Popularion Census, Beijing, 1984. Wang Weizhi Mortality rates for the Chinese population. In China’s Population Yearbook 1985(Edited by Population Research Center), pp. 247-250. Chinese Academy of Social Sciences, 1986. Grant J. P. The State of the World’s Children 1992. Published for UNICEF. Oxford University Press (1992). Jamison D. T. China’s health care system: policies, organization, inputs and finance. In Good Healfh at Low Cost (Edited by Halstead, Walsh and Warren), pp. 21-32. Proceedings of a Conference held at the Bellagio Conference Center, Bellagio, 1985. Young M. E. and Prost A. Child Health in China. World Bank Staff Working Papers, No. 767. The World Bank, Washington, D.C., 1985. Caldwell J. C. Routes to low mortality in poor countries. Populat. and Dev. Rev. 2, 171-220, 1986. Banister J. China’s Changing Population. Stanford University Press, Calif., 1987. Tu Ping The Effects of breast-feeding and birth intervals on child survival. Ph.D. dissertafion, Berkeley, Calif., 1989. Caldwell J. C. Education as a factor in mortality decline: an examination of Nigerian data. Popular. Stud. 3, 3955413, 1979. Berger P. L. and Richard J. N. To Empower People: The Role of Mediating Structures in Public Policy. American Enterprise Institute for Public Policy Research, Washington, D. C., 1977. Mosley W. H. Child survival: research and policy. In Child Survival: Strategies for Research (Edited by Mosley and Chen), pp. 3-23. Popular. Dev. Rev., A supplement to Vol. 10, 1984. Goode W. World Revolution and Family Pattern. Free Press, London, 1963. Parish W. L. and Martin K. W. Village and Family in Conlemporary China. University of Chicago Press, Chicago, 1978. Pastemak B. Marriage and fertility in Tianjin, China: fifty years of transition. Papers of the East- West population institute, No. 99. East and West Center, Population Institute, Hondulin, 1986. DPSB Department of Population Statistics of State Statistic Bureau. China -In-Depth Fertility Survey (Phase I): Principal Report, Vol. 1. Beijing, 1986. Muhuri P. K. and Preston S. H. Effects of family composition on mortality differentials by sex among children in Matlab, Bangladesh. Pomdat. Dev. Rev. 17. 415434, 1991. Guo Guang. Use of sibling data to estimate family mortality effects in Guatemala. Demography 30, 15-32, 1993. Geronimus A. T. and Sanders K. Maternal youth or family background? On the health disadvantages of infants with teenage mothers. Am. J. Epidemiol. 137, 213-225, 1993. Hobcraft J. N., McDonald J. W. and Rutstein S. 0. Demographic determinants of infant and early child mortality: a comparative analysis. Populat. Stud. 39, 363-385, 1985. Singh S. Evaluation of data quality. In The World Fertiliry Survey: An Assessment (Edited by Cleland and Scott), pp. 618643. Oxford University Press, Oxford, 1987. Coale A. J. and Li Shaomin The effects of see misreporting in China on the calculation of mortality rates by very high ages. Demography 2, 293-301, 1991. Allison P. D. Discrete-time methods for the analvsis of event histories. In Sociological Methodology (Edited by Leinhardt S.), pp. 61-98. Jossey-Bass. Calif., 1982.
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APPENDIX Association
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A
of place of delivery and marriage type and communication spouses in Shaanxi, China, 1950-1985 Place of delivery Hospital
Marriage type: and 31.4 Non-arranged neolocality (395) and 11.0 Non-arranged extended (732) 5.0 Arranged and neolocality (21) 4.4 Arranged and extended (136) Discussion of family size: 20.3 Yes (312) 9.9 No (972)
Statistics of association
Home
Total
69.9 (862) 89.0 (5903) 95.0 (397) 95.6 (2945)
100.0
79.6 (1229) 90.1 (8878)
Number of deaths appears in parentheses. x2 test of association: ***P < 0.001 level.
between
x2
df
698***
3
144***
1
100.0 100.0 100.0
100.0 100.0
Infant
and child survival
in Shaanxi,
APPENDIX
China
621
B
Association of mother’s age at birth and year of birth, place of delivery and mother’s level of education, and place of delivery and type of region in Shaanxi, China, 1950-1985 Year of birth
< 17
G1964
4.1
196551969
E (17) 0.2 (6) 0.2 (5) 0.1 (2)
197&1974 197551979 1980-1985
Mother’s 18-20 27.5 (570) 11.6 (“75;) (204) 3.5 E (95)
age at birth 21-27 > 28 56.1 (1162) 62.8 (1402) 57.2 (1578) 66.8 (1446) 74.2 (1605)
Total
12.4 (257) 24.8 (553) 35.2 (970) 29.5 (638) 21.4 (462)
Literate Illiterate Type of region: Plain Hilly
df
100.0 100.0 100.0 100.0 100.0
Place of delivery Mother’s education
Statistics of association X2
1361***
12
Statistics of association
Hospital
Home
Total
25.0 (979) 4.1 (305)
75.0 (2930) 95.9 (7177)
100.0
16.7 (1197) 2.1 (87)
83.3 (5983) 97.9 (4124)
100.0
100.0
100.0
X2
df
1129***
1
566;”
1
Number of deaths appear in parentheses. X2 test of association: ***P < 0.001 level.
APPENDIX
C
Associations of year of birth and spousal communication, year of birth and marriage type, mother’s level of education and spousal communication, and marriage type and mother’s level of education in Shaanxi, China, 1950-1985 Spousal Year of birth
communication
Yes
No
5.6 (117) 9.0 (201) 11.0 (303) 17.4 (376) 25.1 (544)
94.4 (1956) 91.0 (2030) 89.0 (2455) 82.6 (1789) 74.9 (1620)
Mother’s education: Literate 20.1 (786) Illiterate 10.1 (755)
79.9 (3123) 89.9 (6727)
<1964 1965-1969 1970-1974 1975-1979 198tk1985
Marriage Mother’s education: Literacy Illiteracy
Non-arranged and neolocal 17.8 (696) 7.5 (561)
Total
Number of deaths appear in parentheses. X2 test of association: ***P < 0.001 level.
df
100.0
100.0 100.0
100.0 100.0
445***
5
220***
1
100.0 100.0 type
Non-arranged and extended 65.2 (2549) 54.6 (4086)
Statistics of association Y2
Arranged extended 7.9 (73)
Arranged neolocal
Statistics of association Total
15.1
100.0
0 (2490)
100.0
X2
662’+*
df
3